Performance Analysis of Stain Less Steel Conical Probe Using Levenberg-Marquette Algorithm

The multi hole conical probe is extensively employed in the fluid fields for estimating the overall and static pressure and velocity of the vibrant fields. The probe is formed by stainless steel which is utilized in the wind tunnel to determine the static and total pressure of the fluid fields. The probe is engaged to assess their efficiency in execution in the concurrent surroundings at diverse Mach number situations and the yields are calculated according to displacement and stress. The innovative artificial neural network is effectively employed to forecast the accomplishment of the probe by making use of the Levenberg-Marquette algorithm of the artificial neural network, which is applied in the artificial neural network to estimate the yields of the probe and the outcomes are subjected to analysis and contrast with the Conjugate Gradient with Beale (CGB) algorithm, Variable Learning Rate Gradient Descent (GDX) algorithm and Scaled Conjugate Gradient (SCG) algorithm of the artificial neural network. The MATLAB software is performed to assess the efficiency of the artificial neural network for the probe.